Perspective data management for common features of multiple items

ABSTRACT

A computer-implemented method of managing perspective data associated with a common feature in items is disclosed. The method can include identifying a common feature in a first item and a second item, the first item having a set of perspective data and establishing a subset of perspective data associated with the common feature. The method can include associating the subset of perspective with the second item. The method can include determining a set of relevancy scores for the subset of perspective data associated with the common feature and establishing a set of relevant perspective data from the subset of perspective data. The set of relevant perspective data can have relevancy scores outside of a relevancy threshold. The method can include associating the set of relevant perspective data with the second item.

BACKGROUND

Aspects of the present disclosure relate to perspective data management,and more specifically, to incorporating perspective data associated witha common feature in a plurality of items.

When an item, such as a product, consumer good, service, or othersimilar item, is listed for sale on an e-commerce marketplace, ratingsassociated with the item (e.g. average number of stars for the item), insome instances, can be disproportionally affected by user reviews andother similar data. For example, where the item has a relatively smallnumber of user views, subsequent user reviews will have a greater impacton an average rating for the item than for another item having arelatively large number of user reviews. Further, if an item isassociated with relatively few user reviews, the lack of data candecrease a potential buyer's confidence in the accuracy of ratingsassociated with the item.

SUMMARY

According to embodiments of the present disclosure, acomputer-implemented method of managing perspective data associated witha common feature in items is disclosed. The method can includeidentifying a common feature in a first item and a second item, thefirst item having a set of perspective data and establishing a subset ofperspective data associated with the common feature. The method caninclude associating the subset of perspective with the second item. Themethod can also include determining a set of relevancy scores for thesubset of perspective data associated with the common feature andestablishing a set of relevant perspective data from the subset ofperspective data. The set of relevant perspective data can haverelevancy scores outside of a relevancy threshold. The method caninclude associating the set of relevant perspective data with the seconditem.

Embodiments of the present disclosure are directed towards a system formanaging perspective data associated with a common feature in items. Thesystem can include a natural language processing (NLP) unit and a logicdevice. The NLP unit can be configured to identify a common feature in afirst item and a second item, the first item having a set of perspectivedata. The NLP unit can be configured to establish a subset ofperspective data associated with the common feature and determine a setof relevancy scores for the subset of perspective data associated withthe common feature. The logic device can be configured to associate thesubset of perspective with the second item. The NLP unit can beconfigured to establish a set of relevant perspective data from thesubset of perspective data, the set of relevant perspective data havingrelevancy scores outside of a relevancy threshold. The logic device canbe configured to associate the set of relevant perspective data with thesecond item.

Embodiments of the present disclosure are directed towards a computerprogram product for managing perspective data associated with a commonfeature in items. The computer program product including a computerreadable storage medium having program instructions embodied therewith.The program instructions can be executable by a computer to cause thecomputer to perform a method. The method can include identifying acommon feature in a first item and a second item, the first item havinga set of perspective data and establishing a subset of perspective dataassociated with the common feature. The method can include associatingthe subset of perspective with the second item. The method can includedetermining a set of relevancy scores for the subset of perspective dataassociated with the common feature and establishing a set of relevantperspective data from the subset of perspective data. The set ofrelevant perspective data can have relevancy scores outside of arelevancy threshold. The method can include associating the set ofrelevant perspective data with the second item.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts a system for perspective data management, according toembodiments of the present disclosure.

FIG. 2 depicts a diagram of perspective data management for a first itemand second item, according to embodiments of the present disclosure.

FIG. 3 depicts a system architecture for perspective data management,according to embodiments of the present disclosure.

FIG. 4 depicts a flowchart diagram of a method of perspective datamanagement, according to embodiments of the present disclosure.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to perspective data management,more particular aspects relate to incorporating perspective dataassociated with a common feature in a plurality of items. While thepresent disclosure is not necessarily limited to such applications,various aspects of the disclosure may be appreciated through adiscussion of various examples using this context.

While various numbers may be used to identify same named elements withindisclosure, this practice is not intended to limit the scope of thedisclosure. Identified elements in one figure may be the same orsubstantially similar to other same named elements in other figures.

Embodiments of the present disclosure are directed towards a system ofperspective data management. An item, such as a consumer good, product,service, event, location or other similar item can be associated withperspective data to provide prior opinions, experiences, or otherperspectives regarding the item. For example, the item could be a goodor service which is listed on an e-commerce marketplace. Perspectivedata could be associated with the good or service to provide informationto users about the good or service prior to purchase. In certainexamples, the item could be a weekly concert. Perspective data could beassociated with the concert to provide information about the concert tousers before they attend the event.

In embodiments, perspective data could include, but is not limited to,one or more text descriptions of the item. In certain embodiments,perspective data could include one or more rating parameters. The one ormore rating parameters can include, for example, user ratings (e.g. anumber of stars for the item), a percentage of users who had a positiveexperience with the item, or other similar parameters. In embodiments,perspective data can be created by various users. The perspective datacan then be provided to other users to give an impression of userexperiences with the item. That impression can be useful as informationwhich can assist in the decision of whether to purchase, visit, use, orotherwise interact with the item.

In some instances, an item can be associated with a set of perspectivedata. In embodiments, the set of perspective data can be relativelysmall. In such instances, the set of perspective data can bedisproportionally affected by additional perspective data which is addedto the set. For example, where the perspective data includes an averagerating based on one or more individual user ratings, a single additionaluser rating will have a lesser effect on the average rating where theitem has ten thousand previous user ratings than where the item has fiveprevious user ratings. Further, users could have decreased confidence inthe accuracy of the relatively small set of perspective data. Forexample, a user could have greater confidence in a set of perspectivedata including an average rating of four out of five stars, when theaverage rating is generated from ten thousand previous user ratings thanwhen the average rating is generated from only five previous userratings.

In some instances, the item could share one or more common features withanother item. For example, a first item and a second item could each bea different kind of smart phone which share one or more similarcomponents. For example, the one or more similar components couldinclude the type of glass used in the screen, the processor in the smartphone, and the type of battery used. In certain examples, the first andsecond items could share other features such as the same provider, thesame operating system, or other features.

Perspective data related to the common features could be incorporatedbetween the first and second items. For example, perspective dataassociated with the first item, which is related to the type of glassused in the screen or related to other common features between the firstand second items, could be incorporated to become perspective dataassociated with the second item. Thus, the size of a set of perspectivedata associated with the second item could be increased and potentialbuyers could have increased confidence in the perspective data.

In embodiments, the system of perspective data management can beconfigured to identify a common feature in a first item and a seconditem. As described herein, the first item and second item can be varioustypes of items. For example, the item could be a good (e.g. electronics,clothing, or food), a service (e.g. repair service, cleaning service, orchild-sitting), event (e.g. concert, parade, or fair), location (statepark, or monument), or other similar item. In embodiments, features canbe various characteristics of the item. For example, features couldinclude components in an item, the provider or manufacturer of the item,management in charge of the item (where, for example, the item is aservice), the offeror of the item, and other similar features.

Described further herein, the system can utilize natural languageprocessing (NLP) techniques to identify features in the items. Featurescan identified by various techniques. In embodiments, description datacan be associated with the first and second items. Description data caninclude text, tags, or other indicators which describe features of theitem. For example, the description data could be a text listingcomponents included in the item. In certain examples, such as whereitems are listed in an e-commerce marketplace, description data could beincluded with each listed item. In embodiments, the system can parse thedescription data associated with the first and second items to identifycomponents in the items, the provider of the items, and other featuresas described herein. In certain embodiments, a product description canbe parsed using NLP techniques to identify semantically significantwords, such as words having a high-IDF (inverse document frequency)score, repeated throughout description, or trademarked.

In embodiments, the system can determine whether identified features arecommon features. In embodiments, Feature commonality can be determinedby concept matching techniques. Concept matching techniques couldinclude, but is not limited to, semantic similarity, and ontologicalmatching. In embodiments, ontological could be used to map eachparticular feature in an item to a concept. For example, the systemcould be configured to map a first feature from the first item to aconcept (i.e. model number, item number, etc). The system could do thesame with a second feature from the second item, and then determinewhether the concept identifiers are substantially similar.

In certain embodiments, if the concept identifiers are substantiallysimilar, the identified features are common features. In certainembodiments, if the concept identifiers are the same, the identifiedfeatures are common features. For example, where the first and seconditems are tablet computers, each could have a processor chip from afirst provider. The processor chips could be substantially similar ifthey share the same model number. In certain examples, the first andsecond items could each have memory from different providers. The memoryin the first and second items could be substantially similar if theyshare the same capacity and/or memory clock speeds.

In embodiments, the first item can be associated with a set ofperspective data. As described herein, perspective data could includeone or more text descriptions of user perspectives on the first item,and one or more rating parameters. For example, the first item couldhave a set of perspective data including one thousand text descriptionsfrom users along with one thousand rating parameters, such as a numberof stars given to the item out of a total of five stars.

In embodiments, the system can be configured to establish a subset ofperspective data associated with the common feature. In embodiments, thesystem can use NLP techniques to parse the set of perspective dataassociated with the first item to identify perspective data which isrelated to the common features. In embodiments, perspective data couldbe logically segmented to natural boundaries (i.e. sentences, paragraph,section, etc.) and evaluated for a LAT (Lexical Answer Type) and/or corefocus of that segment. The LAT and/or focus could be used to determinewhether a segment was associated with the common feature. For example,where the common feature is the processor in the item, the system couldanalyze text reviews of the first item to identify one or more textreviews which discuss the processor. The system can then establish asubset of the perspective data which includes perspective data relatedto the common features.

In embodiments, the system can be configured to determine a set ofrelevancy scores for the subset of perspective data. Relevancy scorescan be applied to the subset of perspective data to filter out certainperspective data associated with the common feature but which does notmeet a threshold of relevancy to be incorporated with perspective dataassociated with the second item. The relevancy scores can be calculatedbased on various factors. In embodiments the relevancy score can becalculated based on perspective data metadata. Perspective data metadatacan include data about the perspective data. For example, perspectivedata metadata could include the length of a text description, whetherthe perspective data originated from a verified user of the item, thenumber of users which found the perspective data helpful, the origin ofthe perspective data, or other similar data.

In embodiments, the system can be configured to establish a set ofrelevant perspective data from the subset of perspective data. Describedfurther herein, the set of relevant perspective data can establishedfrom the subset of perspective data having relevancy scores outside of arelevancy threshold. In embodiments, the system can be configured toassociate the set of relevant perspective data with the second item.

Referring now to FIG. 1, a system 100 for perspective data managementcan be seen according to embodiments of the present disclosure. Inembodiments, the system 100 can include a processor 102, memory 112, andI/O (input/output) devices 126.

The processor 102 can execute instructions stored in memory 112 andperform various functions in the computer processing system 100. Theprocessor 102 can include CPU cores 104A, 104B. In embodiments, theprocessor 102 can contain a plurality of CPU cores. In certainembodiments, the processor 102 can contain a single CPU core. Each ofthe CPU cores 104A, 104B can include registers 106A, 106B, and L1 cache108A, 108B. The CPU cores 104A, 104B can retrieve and executeinstructions from memory 112 and provide logic functions for theprocessor 102. The registers 106A, 106B and L1 cache 108A, 108B canprovide storage for data that frequently accessed in each CPU core 104A,104B. The processor 102 can also include L2 cache 110. The L2 cache 110can be communicatively connected to each of the CPU cores 104A, 104B andcan provide shared storage for data in the processor 102.

In embodiments, the system 100 can contain multiple processors 102typical of a relatively large system. In certain embodiments, thecomputer system 100 can be a single processor system. The processor 102can be various types of processors including, but not limited to digitalsignal processor (DSP) hardware, network processor, application specificintegrated circuit (ASIC), field programmable gate array (FPGA), orother types of processors. The memory 112 can be coupled to theprocessor 102 via a memory bus 122.

The memory 112 can include a random-access semiconductor memory, storagedevice, or storage medium (either volatile or non-volatile) for storingor encoding data and programs. The memory 112 can be conceptually asingle monolithic entity, but in other embodiments the memory 112 can bea more complex arrangement, such as a hierarchy of caches and othermemory devices. The memory 112 can store data, instructions, modules,and other types of information, hereafter collectively referred to as“memory elements.” Although the memory elements are illustrated as beingcontained within the memory 112, in certain embodiments some or all ofthem can be on different devices and can be accessed remotely, e.g., viaa network.

The system 100 can use virtual addressing mechanisms that allow theprograms of the computer system 100 to behave as if they only haveaccess to a large, single storage entity instead of access to multiple,smaller storage entities. Thus, while the memory elements areillustrated as being contained within the memory 112, these elements arenot necessarily completely contained in the same storage device at thesame time. Further, although the memory elements are illustrated asbeing separate entities, in other embodiments some of them, portions ofsome of them, or all of them can be packaged together.

In embodiments, the memory elements can include a perspective datamanagement application 113, and a question answering application 114having an NLP application 116. The memory elements can also includeperspective data 118. In embodiments, the perspective data managementapplication 113 can instruct the system 100 to perform embodiments ofthe present disclosure, as described herein. In certain embodiments, theperspective data management application 113 can use the questionanswering application 114 to perform embodiments of the presentdisclosure.

For example, in embodiments, the question answering application 114could receive one or more questions and construct answers by querying astructured or unstructured body of data. In embodiments, the NLPapplication 116 can be used to evaluate questions posed in naturallanguage format. In embodiments, the NLP application 116 can also beused to analyze/search the structured or unstructured body of data toconstruct an answer to questions. In embodiments, the question answeringapplication 114 can then extract, from the body of data, one or morecandidate answers to the question. In embodiments, the candidate answerscan be scored and ranked by the question answering application 114 toproduce a ranked list of answers with associated confidence values.

In embodiments, the question answering application 114 could receive aquestion asking to identify any common features shared between a firstitem and a second item. The NLP application 116 could be used tounderstand the question posed. Further, the question answeringapplication 114 could use the NLP application 116 to parse descriptiondata associated with the first and second item to identify commonfeatures. The NLP application 116 could be used to parse perspectivedata 118 associated with the first item to identify a subset ofperspective data 118 associated with the common features. Theperspective data 118 can be the same or substantially similar asdescribed herein. In embodiments, the perspective data 118 can bestructured or unstructured data serving as a body of data for thequestion answering application 114.

The processor 102 can also communicate with one or more I/O devices 126via an I/O bus 124. The I/O devices 126 can include, but are not limitedto, devices such as a keyboard, a pointing device, a display, one ormore devices that enable a user to interact with the computer system100, and various devices (e.g., network interface card, modem, etc.)that enable the computer system 100 to communicate with one or moreother computing devices. It should be understood that other suitablehardware and software components can be used in conjunction with thecomputer system 100.

Referring now to FIG. 2, a diagram of perspective data management 224for a first item 202A and second item 202B can be seen according toembodiments of the present disclosure. The first item and second item202A, 202B can each have description data 204A, 204B and perspectivedata 214A, 214B. For example, the first item 202A and the second item202B could each be listed on an e-commerce marketplace. As a part of thelisting the e-commerce marketplace could have description data 204A andperspective data 214A associated with the first item 202A anddescription data 204B and perspective data 214B associated with thesecond item 202B. In certain embodiments, the first item 202A and seconditem 202B could be listed in other suitable locations.

As seen in FIG. 2, description data 204A, 204B can include dataidentifying features in the first and second item 202A, 202B. Forexample, description data 204A can include data identifying a firstofferor 206, a first component 208, and a first provider 210 as featuresof the first item 202A. Description data 204B can include dataidentifying the first offeror 206, the first component 208, and a secondprovider 212 as features of the second item 202B.

As described herein, the perspective data 214A, 214B can include textreviews of the items 202A, 202B, a rating parameter, or otherinformation. As shown in FIG. 2, perspective data 214A can include afirst review 216 and a second review 220. The first review 216 caninclude first review metadata 218. The second review 220 can includesecond review metadata 222. Review metadata 218, 222 can include dataabout the individual reviews 216, 220. For example, review metadata 218,222 could include the identity of the reviewer, whether or not otherusers found the review 216, 220 helpful, whether the review 216, 220 wascreated by a verified user of the item 202A, 202B, and other metadata.In certain embodiments, metadata regarding the identity of the reviewercan include the number of reviews created by the reviewer, the contentof reviews created by the reviewer and other information.

Perspective data management module 224 can be the same or substantiallysimilar as the perspective data management application 113 (FIG. 1). Inembodiments, the perspective data management module 224 can identify acommon feature in the first item 202A and the second item 202B. Thecommon feature can be the same or substantially similar as describedherein. In FIG. 2 for example, the first item 202A and the second item202B share features of the first offeror 206 and the first component208. As described herein, the perspective data management module 224 canparse the description data 204A, 204B to identify features in the items202A, 202B. As described herein, if the items 202A, 202B are associatedwith concepts (such as model number, item number, etc.) which are thesame or substantially similar, then they can be common features. In FIG.2, the perspective data management module can identify that the commonfeatures between the first item 202A and the second item 202B includethe offeror 206 and the component 208.

The perspective data management module 224 can establish 226 a subset ofperspective data 228 associated with the common features. As describedherein, the perspective data management module 224 can parse perspectivedata 214A for the first item 202A and identify perspective data which isrelated to the common features. For example, in FIG. 2, the perspectivedata management module 224 can identify review 216 as being associatedwith the offeror 206 and review 220 as being associated with components208. The perspective data management module 224 can establish 226 asubset of perspective data 228 associated with the common features ofthe offeror 206 and/or the component 208.

The perspective data management module 224 can determine a relevancyscore for perspective data in the subset of perspective data 228. Forexample, the perspective data module 224 can determine a relevancy scorefor the first review 216 and a relevancy score for the second review220. In embodiments, the relevancy score can be determined based onperspective data metadata. For example, review metadata 218, 222 can beused to determine relevancy scores for the first review 216 and thesecond review 220. For example, the second review 220 could havemetadata 222 indicating that the review was made by a verified user ofitem 202A and that the person who made the first review 216 had aplurality of other reviews for various items. This metadata couldindicate that the second review 220 was relatively trustworthy. Thus,the perspective data management module 224 could generate a relativelyhigh score for the second review 220. In certain examples, the firstreview 216 could have metadata 218 indicating that the review was madeby an unverified user of item 202A and that the person who made thefirst review 216 had no other reviews. The perspective data managementmodule 224 could generate a relatively low score for the first review216.

If the relevancy score for perspective data is outside of a relevancythreshold, then the perspective data management module 224 can determinethat the perspective data is relevant. The perspective data managementmodule 224 can establish 230 a set of relevant perspective data 232 fromthe subset of perspective data 228. The set of relevant perspective data232 can be established from perspective data which has a relevancy scoreoutside of the relevancy threshold, as described herein.

If the relevancy score for perspective data is within the relevancythreshold, then the perspective data management module 224 can determinethat the perspective data is irrelevant.

The perspective data management module 224 can associate 234 the set ofrelevant perspective data with the second item 202B. For example thesecond review 220, after being determined to be associated with a commonfeature and determined to be relevant, can be associated with theperspective data 214B for the second item 202B. Thus, a user seeinginformation regarding the second item 202B will be presented withperspective data 214B having the second review 220. Thus, the seconditem 202B has incorporated perspective data from the first item 202A toincrease the sample size of perspective data 214B, as described herein.

Referring now to FIG. 3, a system architecture 300 for perspective datamanagement can be seen according to embodiments of the presentdisclosure. As shown in FIG. 3, in certain embodiments, the systemarchitecture 300 can include a common feature identification system 310.The common feature identification system 310 can include a descriptiondata parsing module 312, a shared characteristic determination module314, a database 316, and a common feature selection module 318.

The description data parsing module 312 can be configured to use naturallanguage processing techniques to analyze semantic and syntactic contentof a set of description data. The set of description data can be storedin a database 316 accessible to the common feature identification system310. In response to parsing the set of description data, the sharedcharacteristic determination module 314 can be configured to determine aset of shared characteristics in the set of description data. The commonfeature selection module 318 can be configured to select a first sharedcharacteristic as the common feature.

As described herein, in certain embodiments, the natural languageprocessing techniques can be configured to identify features in of theset of description data and determine whether features are the same orsubstantially similar. In embodiments, features can be identified byparsing a semi-structure product specification. In certain embodiments,features can be identified by processing unstructured description datalooking for high-IDF (inverse document frequency) terms and/or conceptsof particular merit from a known ontology of terms associated withfeatures. Accordingly, in certain embodiments, the common feature can bedetermined based on the characteristics in the set of description data.

In embodiments, the system architecture 300 can include a groupingsystem 320. The grouping system 320 can include a perspective datasorting module 322. The grouping system 320 can be configured to groupperspective data from a set of perspective data into a subset ofperspective data based on the common feature. As described herein, thegrouping system can sort perspective data which is associated with thecommon feature into the subset of perspective data.

In embodiments, the system architecture 300 can include a relevancyscore determination system 330. The relevancy score determination system330 can include a group content parsing module 324, a relevancy scorecalculation module 326, and a relevancy score assignment module 328. Thegroup content parsing module 324 can be configured to parse, using thenatural language processing technique, semantic and syntactic content ofthe subset of perspective data. Based on semantic content, syntacticcontent, and metadata for the subset of perspective data, the relevancyscore calculation module 326 can be configured to calculate a set ofrelevancy scores for perspective data in the subset. In response tocalculating the set of relevancy scores, the relevancy score assignmentmodule 328 can assign the relevancy scores to the perspective data.

In embodiments, the system architecture 300 can include a perspectivedata incorporation system 340. The review establishing system 340 caninclude a relevancy score/threshold determination module 342, and afiltering module 344.

The relevancy score/threshold determination module 342 can be configuredto determine whether relevancy scores for the subset of perspective datais outside of a relevancy threshold. Accordingly, the filtering module344 can be configured to filter the out perspective data which has arelevancy score within the relevancy threshold in order to establish aset of relevant perspective data.

Referring now to FIG. 4 a flowchart diagram of a method 400 can be seenaccording to embodiments of the present disclosure. In operation 402, asystem can identify a common feature among a first item and a seconditem. The first item and second item can be the same or substantiallysimilar as described herein. The common feature can be the same orsubstantially similar as described herein. In embodiments, the commonfeature is a feature in the first item and the second item which are thesame. In certain embodiments, the common feature is a feature in thefirst and second item which are substantially similar.

In operation 404, the system can identify a set of perspective dataassociated with the first item. The set of perspective data can be thesame or substantially similar as described herein. In embodiments, theset of perspective data can include text reviews and rating parameters.

In operation 406, the system can establish a subset of the perspectivedata that is associated with the common feature. As described herein, asystem of perspective data management can parse description dataassociated with the first and second items to identify one or morecommon features between the items. In operation 408, the system candetermine relevancy scores for the subset of perspective data. Inembodiments, the relevancy scores can be determined based on metadataassociated with the perspective data as described herein.

If relevancy scores are outside of a relevancy threshold then, indecision block 410, the method can progress to operation 412. Inoperation 412, the system can establish a set of relevant perspectivedata. If relevancy scores are not outside of the relevancy thresholdthen, in decision block 410, the method 400 can terminate as none of theperspective data has a relevancy score sufficient to be incorporatedbetween the first and second items. In operation 414, the system canassociate the set of relevant perspective data with the second item.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1-7. (canceled)
 8. A system for managing perspective data associatedwith a common feature in items, the system comprising: a naturallanguage processing unit configured to: identify a common feature in afirst item and a second item, the first item having a set of perspectivedata; establish a subset of perspective data associated with the commonfeature; determine a set of relevancy scores for the subset ofperspective data associated with the common feature; and establish a setof relevant perspective data from the subset of perspective data, theset of relevant perspective data having relevancy scores outside of arelevancy threshold; and a logic device configured to: associate the setof relevant perspective data with the second item.
 9. The system ofclaim 8, wherein: the natural language processing unit is furtherconfigured to: determine a set of relevancy scores for the subset ofperspective data associated with the common feature; establish a set ofrelevant perspective data from the subset of perspective data, the setof relevant perspective data having relevancy scores outside of arelevancy threshold; and wherein the logic device is further configuredto: associate the set of relevant perspective data with the second item.10. The system of claim 9, wherein: the natural language processing unitbeing configured to determine the set of relevancy scores for the subsetof perspective data further includes being configured to: parse, using anatural language processing technique configured to analyze semantic andsyntactic content, the subset of perspective data; calculate, based onsyntactic content, semantic content, and metadata for the subset ofperspective data, the set of relevancy scores; and assign the set ofrelevancy scores to the subset of perspective data.
 11. The system ofclaim 8, wherein: the natural language processing unit being configuredto identify the common feature of the first and second item furtherincludes being configured to: parse, using a natural language processingtechnique configured to analyze semantic and syntactic content, a set ofdescription data associated with the first and second items; determine,in response to parsing the set of description data, a set of sharedcharacteristics in the set of description data; and select, from the setof shared characteristics, at least one shared characteristic as thecommon feature.
 12. The system of claim 11, wherein: the naturallanguage processing unit being configured to determine a set of sharedcharacteristics in the set of description data further includes beingconfigured to: determine, based on semantic information analyzed by thenatural language processing technique, that the first item and seconditem have at least one characteristic which is substantially similar;and include the at least one characteristic which is substantiallysimilar in the set of shared characteristics.
 13. The system of claim12, wherein: the natural language processing unit being configured todetermine, based on semantic information analyzed by the naturallanguage processing technique, that the first item and second item haveat least one characteristic which is substantially similar, includesbeing configured to: map a first feature from the first item to a firstconcept; map a second feature from the second item to a second concept;determine whether the first concept and second concept are substantiallysimilar; and determine that the first item and second item have at leastone characteristic which is substantially similar in response todetermining that the first concept and second concept are substantiallysimilar.
 14. The system of claim 13, wherein: the natural languageprocessing unit being configured to determine whether the first conceptand second concept are substantially similar includes being configuredto: determine whether the first concept and second concept are the same;and determine that the first concept and second concept aresubstantially similar in response to determining that the first conceptand second concept are the same.
 15. A computer program product formanaging perspective data associated with a common feature in items, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, the program instructionsexecutable by a computer to cause the computer to perform a methodcomprising: identifying a common feature in a first item and a seconditem, the first item having a set of perspective data; establishing asubset of perspective data associated with the common feature; andassociating the subset of perspective data with the second item.
 16. Thecomputer program product of claim 15, wherein the method furthercomprises: determining a set of relevancy scores for the subset ofperspective data associated with the common feature; establishing a setof relevant perspective data from the subset of perspective data, theset of relevant perspective data having relevancy scores outside of arelevancy threshold; and associating the set of relevant perspectivedata with the second item.
 17. The computer program product of claim 16,wherein: determining the set of relevancy scores for the subset ofperspective data further includes: parsing, using a natural languageprocessing technique configured to analyze semantic and syntacticcontent, the subset of perspective data; calculating, based on syntacticcontent, semantic content, and metadata for the subset of perspectivedata, the set of relevancy scores; and assigning the set of relevancyscores to the subset of perspective data.
 18. The computer programproduct of claim 15, wherein: identifying the common feature of thefirst and second item further includes: parsing, using a naturallanguage processing technique configured to analyze semantic andsyntactic content, a set of description data associated with the firstand second items; determining, in response to parsing the set ofdescription data, a set of shared characteristics in the set ofdescription data; and selecting, from the set of shared characteristics,at least one shared characteristic as the common feature.
 19. Thecomputer program product of claim 18, wherein: determining a set ofshared characteristics in the set of description data further includes:determining, based on semantic information analyzed by the naturallanguage processing technique, that the first item and second item haveat least one characteristic which is substantially similar; andincluding the at least one characteristic which is substantially similarin the set of shared characteristics.
 20. The computer program productof claim 19, wherein: determining, based on semantic informationanalyzed by the natural language processing technique, that the firstitem and second item have at least one characteristic which issubstantially similar includes: mapping a first feature from the firstitem to a first concept; mapping a second feature from the second itemto a second concept; determining whether the first concept and secondconcept are substantially similar; and determining that the first itemand second item have at least one characteristic which is substantiallysimilar in response to determining that the first concept and secondconcept are substantially similar.